In a surveillance system, a camera operator follows an object of interest by moving the
camera, then gains additional information about the object by zooming. As the active vision
field advances, the ability to automate such a system is nearing fruition. One hurdle limiting
the use of object recognition algorithms in real-time systems is the quality of captured
imagery; recognition algorithms often have strict scale and position requirements where if
those parameters are not met, the performance rapidly degrades to failure. The ability of
an automatic fixation system to capture quality video of an accelerating target is directly
related to the response time of the mechanical pan, tilt, and zoom platform—however the
price of such a platform rises with its performance. The goal of this work is to create a
system that provides scale-invariant tracking using inexpensive off-the-shelf components.
Since optical zoom acts as a measurement gain, amplifying both resolution and tracking
error, a new second camera with fixed focal length assists the zooming camera if it loses
fixation—effectively clipping error. Furthermore, digital zoom adjusts the captured image
to ensure position and scale invariance for the higher-level application. The implemented
system uses two Sony EVI-D100 cameras on a 2.8GHz Dual Pentium Xeon PC. This work
presents experiments to exhibit the effectiveness of the system.